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Do relationships matter in the corporate bond market?

Abstract

We examine the impact of information networks on the pricing of corporate bonds. Using the

location of issuers, their bookrunners and institutional investors as a proxy for the quality of such

networks, we find that issuers based in central, urban areas have significantly lower at-issue yield

spreads compared to their remote, rural counterparts. Prior underwriting relationships as well as

proximity between economic agents in the network result in a spread reduction, especially for

remote and risky issuers. Our findings provide evidence of a new channel through which local

information networks can impact firm value, namely the pricing of corporate bonds.

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"We're not looking for just one transaction, we're looking for a relationship that is going to span many

years”. Chris Wood, head of high yield capital markets for SunTrust Robinson Humphrey, Bond

Bookrunning Group.

I. Introduction

The relationship between corporations, security underwriters and investors is a cornerstone of

financial markets. For instance, Burch et al. (2005), Binay et al. (2007), and Huang et al. (2008),

among others show that relationships such as this create unique networks of issuers and

institutional investors who tend to stay loyal to their main investment bank over the long term.

This suggests that, much like a social network, both issuers and investors could self-segment

through an affiliation with particular investment banks. Despite the importance of issuer-

underwriter-investor relationships, empirical research provides little evidence on the role of such

information networks in the pricing of securities. In this paper, we exploit the predominance of

institutional investors in the U.S. corporate new bond issues market to examine whether such

information networks have a direct effect on the pricing of securities in general, and bonds in

particular.1

Compared to equities, the strong institutional nature as well as lower liquidity and long term

investment horizon of bond investors, make corporate bonds an ideal laboratory to study the effect

of information networks on asset prices. The book-building process and general marketing efforts

during corporate bond issues create a segmented information flow directed at targeted groups of

long term investors. The reliance on such relationship specific knowledge becomes particularly

important for bondholders in the absence of formal road shows or frequent conversations with the

1 The corporate bond market is almost exclusively driven by institutional investors. For example, Bessembinder, Kahle, Maxwell and Xu (2009)

report that trades of $100,000 or greater, account for 96.7% of bond trading volume. The presence of institutions is even more pronounced in the

primary market, where individual investors typically do not have an opportunity to purchase a new issue until after the initial announcement.

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issuing firm’s management during the book-building process.2 Underwriters also offer their clients

information intensive activities such as market making, analyst coverage, and advice on mergers

and acquisitions that often lead to repeat transactions between bond issuers and their lead

underwriter (Yasuda, 2005). Over time, these information sharing activities create suitable

conditions for both issuers and investors to form a relationship with their main investment bank.

To examine whether such relationships matter in the pricing of corporate bonds, we rely upon

the existing literature to construct measures of the strength of information networks between

issuers, underwriters and institutional investors. Because people are most likely to network and

share information with those that live or work nearby (see, e.g., Bayer et al., 2008 and Hong et al.,

2008), we consider the location of an issuing firm’s headquarters vis-à-vis its lead underwriter and

institutional investors as the main facet of the quality of information networks.

Specifically, we ask whether an issuer’s geographic location through the formation of local

information networks affects the pricing of debt. For example, decreased observability of remotely

headquartered companies and their distance from central locations that are characterized by large

concentrations of investment banks and institutional investors could create lower potential for

information sharing and networking opportunities through repeated interactions. As a result,

underwriters may find it more difficult to assess an issuer’s creditworthiness, and market and sell

its securities. In contrast, centrally located issuers are more likely to be closer to their lead

2 In their recent presentation to the SEC, the Credit Roundtable, a group of large fixed income institutional investors, describes the current practices

of corporate bond underwriting and distribution: “when new corporate bonds are issued, institutional investors’ ability to conduct proper diligence

is significantly diminished without conference calls, road show slides or presentations… From the time underwriters announce the new issue,

investors have as little as 15 minutes to evaluate prospectus terms (often which are not available), the issuer’s credit history, and pricing

expectations… For the large majority of new issues, there is no conference call with the management of the issuer… Covenant description may be

imprecise or confusing, with risk factors that are often generic and lack company specific details”. (The Credit Roundtable “Current Practices of

the Corporate Bond Underwriting & Distribution Process and Recommendations for Improvement”, presentation to the SEC, March 3 2009).

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underwriter, who has better knowledge of and more repeat relationships with local institutional

investors that can be targeted during the book-building process. These local investment banks may

also have more intimate issuer-specific knowledge than remote investment banks through

familiarity with the local economy and personal relationships with the issuer’s management.

Indeed, Figure 1 shows a clear concentration of bond issuers, institutional investors and

bookrunners around large, central metropolitans. In this paper, we ask whether such clustering of

economic agents can benefit bond issuers through the formation of local information networks.

To determine the effect of location, our main proxy for the quality of information networks,

on the cross section of at-issue bond yield spreads, we start with a sample of US corporate bonds,

issued during 1998-2008. We follow Ivkovic and Weisbenner (2005) and Loughran and Schultz

(2005), among others, and aggregate issuers’ headquarters by metropolitan statistical areas

(MSAs), classifying firms as either Urban or Rural, based on the size and centrality of the

metropolitan where the firm is headquartered and its distance from major population and economic

activity clusters.

We find that firms headquartered in remote, sparsely populated areas (Rural) exhibit

significantly higher at-issue bond yield spreads than firms headquartered in central (Urban) areas,

especially for small and longer maturity issues, for which the spread differential can be as much

as 33 basis points. This is consistent with the notion that firms that are based in large, central areas

that are home to more investor networks offer issuers a comparative pricing advantage compared

to their remote, less observable counterparts.

We also find that the proximity of issuers to their lead underwriter (measured as the distance

between the headquarters of the issuer and that of their bookrunner) is highly beneficial to Rural

firms, firms issuing smaller size and longer maturity debt, and firms that issue less frequently.

Similarly, we find that higher levels of local institutional ownership with proximity to the issuer

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and/or the lead underwriter, result in a significant reduction in at-issue yield spreads for Rural and

riskier issuers. These findings are consistent with the benefits of localized information networks

that give both investors and underwriters a comparative advantage in monitoring and assessing

soft information about local issuers (Butler, 2008).

Next we examine the impact of recent prior relationships between an issuer, its bookrunner

and institutional investors on the pricing of newly issued bonds. We find that the existence of a

recent prior underwriting relationship is highly beneficial for remote Rural issuers, as well as

issuers of riskier debt, resulting in a significant at-issue spread reduction compared to similar non-

relationship issues.

The existence of a statistically significant spread differential between bonds issued by Rural

and Urban companies is robust to a host of endogeneity and sensitivity tests, including an

instrumental variable, alternative measures of geographic location, sample selection criteria, firm

and industry controls, firm specific information and governance proxies. One of our most

convincing tests is based on a natural experiment using brokerage closures and mergers that

directly forced some firms to lose analyst coverage. Using a difference-in differences approach,

we show that while an exogenous loss of coverage results in an increase in the cost of debt for both

Rural and Urban issuers compared to their respective control groups that did not suffer coverage

loss, the spread increase is significantly more pronounced for Rural companies, indicating that

they are more adversely affected by this exogenous, forced loss of information intermediaries.

Finally, we examine whether our results hold for traded bonds. In complete markets, localized

networks of information should not affect bond pricing and thus, mispricing due to location should

be arbitraged away by sophisticated institutional bondholders in the secondary bond market.

However, if the distance between issuers and investors results in information friction and market

segmentation through the creation of localized information networks, we should continue to

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document cross sectional variation in bond prices even for traded bonds. Using data on traded

bonds and controlling for firm, bond and macroeconomic characteristics, our results continue to

hold.

Our paper adds to several streams of research in the finance literature. First, we show that

networks play a role in the determination of asset prices. Our evidence is consistent with the work

of Asker and Ljungqvist (2010), Fernando et al. (2012), Grullon et al. (2012) and others, which

shows that investment banking relationships create issuer and/or investor networks. Our paper

provides evidence of a new channel of local networks through which the various investment bank

relationships can add value – the pricing of corporate bonds.

Second, our results are related to the literature on proximity investment (e.g., Coval and

Moskowitz, 1999, 2001). This literature has mostly focused on the equity side, showing that

investors tend to hold the stocks of firms located nearby because of information advantages,

familiarity, and/or local social interactions.3 We focus on the debt side and show that it has equally

important implications on firms’ cost of capital. This is particularly important given the size of the

corporate bond market, and the fact that over the last decade bond financing has become one of, if

not, the most important sources of external financing for US corporations.4

Finally, our paper is related to an extensive body of research about the benefits associated with

the agglomeration of economic agents across geographic or industry clusters that come from

3 Coval and Moskowitz (2001), Ivkovic and Weisbenner (2005), Ivkovic et al. (2008), and Colla and Mele (2010) conclude that local investors

have an informational advantage. Huberman (2001) shows that people tend to invest in the familiar. Grinblatt and Keloharju (2001), Hong et al.

(2004, 2005), and Brown et al. (2008) find that social interaction among investors is important for investment decisions. Recent research also

studies the implication of local bias for equity returns or prices. For example, Pirinsky and Wang (2006) show that the returns of firms located

within the same geographical vicinity co-move more strongly; Feng and Seasholes (2004) show correlated trading when investors are closer to the

headquarters; and Garcia and Norli (2012) find that locality is associated with higher stock returns.

4 As of March 2013 there was $9.2 trillion of US corporate debt outstanding (source: http://www.sifma.org/research/statistics.aspx).

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learning, intellectual spillover, and access to informal “soft” information through interactions with

local professionals (see, e.g., Almazan et al., 2007; Christoffersen and Sarkissian, 2009; Ellison et

al., 2010; and Almazan et al., 2010). Our study adds to this literature by showing that local

economic clusters also play a role in the pricing of corporate bonds.

Overall, our findings suggest that the pricing of corporate bonds is strongly influenced not only

by firm and issue characteristics, but also by the business environment in the location where the

firm is headquartered. As such, they challenge the classical view of corporate finance that

bondholders have arm’s length transactions with firms that are only based on publically available

information (e.g., Myers, 1977; Myers and Majluf, 1984; and Dittmar and Thakor, 2007).

The remainder of the paper is organized as follows. In section II we describe the data, variables

and methodology. Section III presents the results. Section IV provides robustness tests and Section

V concludes.

II. Data

A. Sample and Methodology

Our data are from multiple sources and cover the 1998-2008 time-period. We construct the

information networks proxies using location and prior relationship measures. Our main location

measure is based on whether the issuer is headquartered in a large and central area (Urban) or in

a remote, sparsely populated one (Rural). To classify firms as Urban or Rural, we follow Coval

and Moskowitz, (1999) and Loughran and Schultz (2005), and others, and use a company’s

headquarters as a proxy for its location. Headquarters locations are from Compustat, SDC and

Hoover’s and for relocations we use various SEC filings, where applicable. We find the latitude

and longitude for each firm’s headquarters using the US Census Bureau’s Gazetteer city-state files,

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and calculate the distance between the headquarters and the largest US metropolitan areas of at

least 1 million people as defined by the 2010 Census.

Following Loughran and Schultz (2005) and John, Knyazeva and Knyazeva (2011), among

others, a company is classified as an Urban firm if its headquarters MSA population size is 1

million or above. A company is classified as Rural if its headquarters is at least 250 kilometers

away from the nearest Urban firm in MSAs with population size under 1 million.

The main bond data source is the Securities Data Corporation (SDC) New Issues database. To

calculate bond yield spreads, we use the risk-free term structure of interest rates taken from

Bloomberg, including the monthly treasury benchmark yields for 2, 3, 5, 7, 10, and 30 year coupon

bonds. Following conventional sample selection criteria, we exclude firms headquartered and

incorporated outside the US and firms with asset size below $20 million. For debt issues to be

included in our analysis, data on the firm’s headquarters location, leverage, assets, proceeds and

par amount, at-issue yield, maturity date, and lead underwriter of the firm’s fixed coupon rate for

straight public debt securities must be available. After imposing the above selection criteria, our

final sample is comprised of 4,328 new issues made by 904 firms (794 Urban and 110 Rural), with

Rural and Urban companies making 409 and 3,919 issues, respectively. To calculate the distance

between an issuer’s headquarters and the location of its lead underwriter we obtain the name of

the lead underwriter(s) from SDC and hand collect information on its principal location of

business.

We obtain institutional bond investors data from Lipper’s eMAXX fixed income database

which provides information on their location and quarterly bond ownership. We only include firms

with complete information on par amount, coupon rate, issue and maturity date, address and US

location during our sample period, resulting in a sample size of 2,424 bond holdings by

institutional investors. We then calculate the distance between the location of each issuer and that

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of its institutional investors, and the distance between the issuer’s bookrunner and each of its

institutional investors. Data on analyst following are from I/B/E/S, outside blockholders data is

from CDA Spectrum, and covenant provisions are from Fixed Income Securities (FISD). We also

obtain the Gompers et al. (2003) governance index (GIM Index) from Metrick’s website.

Secondary market bond trade data are from several sources. Transaction information such as

trade date, price, and underlying yield corresponding to all bond transactions between January 1,

2005 and December 31, 2008 are from the TRACE database. Share prices and accounting data, as

well as issuer headquarters location information are from Bloomberg and the historical location

files on Compustat. Treasury yields adjusted to constant maturities are from the Federal Reserve,

and LIBOR rates are from the British Bankers’ Association. These data are then merged to yield a

sample consisting of 2,215 domestic bonds issued by 754 firms that meet the criteria outlined

above. Finally, we supplement our traded bond dataset with data on 5-year CDS from Bloomberg

and obtain data on 298 firms with CDS data for 1,284 bonds for the January 2005- December 2008

time-period.

B. Description of Variables

The dependent variable is the at-issue yield spread (Spread), defined as the difference between

the yield to maturity on a coupon paying corporate bond and the yield to maturity on a coupon

paying government bond with the same maturity date. We use bond-specific, firm-specific and

demographic control variables in our analysis. Bond related measures include: credit rating

(Rating), issuance proceeds (Proceeds), time to maturity in years (Time to maturity), a dummy

variable to denote issues with restrictive covenants (Covenants), bond seniority (Senior) and

callable (Call) dummy variables, a dummy variable to denote high yield, non-investment grade

bonds with ratings below Baa3 (High yield issues), and a dummy variable to denote non-rated

bond issues (Non rated).

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A firm’s credit rating is measured by Moody’s bond ratings at the issue date. Similar to Klock

et al. (2005) and others, we compute bond ratings using a linear conversion process in which Aaa

or AAA ratings are assigned a value of 1 and B3 or B- ratings are assigned a value of 16. For

example, a firm with an Aa1 Moody’s rating would receive a score of 2; a firm with Aa2 rating

would receive a score of 3; and so on. Thus higher numerical bond ratings denote higher credit

risk. All bond issuance related data are obtained from SDC.

Our firm-specific variables are primarily from Compustat and include headquarters location,

issue size (Size) measured as the natural log of the issue proceeds; firm leverage (Leverage),

calculated as the ratio of long-term debt to total assets; firm age (Age) defined as the difference in

years between the current year and the year of the company’s incorporation; and firm profitability

(ROA and market-to-book ratio). We calculate ROA as the ratio of earnings before interest, tax,

depreciation and amortization, divided by total assets. For the market-to-book ratio, we use the

end of the previous year’s CRSP market value of equity scaled by the prior fiscal year’s book value

(defined as book value of equity plus balance-sheet deferred taxes and investment credit minus the

book value of preferred stock).

To control for issuance frequency, we create a dummy variable for one time bond issuers

during the sample period (One time issuer). Following Fang (2005), we create a dummy variable

to denote prestigious lead bond underwriters in our sample (Prestigious underwriters). We also

control for the listing location of issuing firms by creating a dummy variable that is equal to 1 if

the firm is listed on Nasdaq, and 0 otherwise (Nasdaq).

To address the issue of information asymmetry we use the number of analysts following the

firm (Number of Analysts). Malloy (2005) and Bae et al. (2006), among others, show that the

information production role of analysts increases with their proximity to target firms. Analysts

coverage is defined as the number of analysts reporting current fiscal year annual earnings

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estimates prior to the end of fiscal year t. Firms without I/B/E/S coverage are assigned a value of

zero analysts.

Better governed firms are expected to be less affected by distance from large cities and distance

from investors and underwriters who all serve a monitoring role. As such, we control for

institutional and governance related variables, namely, the percentage of outside blockholdings (%

outside blockholders) and the level of antitakeover defenses (GIM index). The presence of

institutional blockholders who own at least 5% of the firm’s stock is associated with the monitoring

of management (Shleifer and Vishny, 1997). To construct this variable, we match the percentage

of shares held by outside blockholders with our sample using data from Dlugosz et al. (2006).5 If

local institutions hold shares of Rural firms, these firms should face fewer governance problems

that would mitigate informational differences between Rural and Urban companies thereby,

reducing the likelihood of finding a significant location effect.

To account for factors associated with the issuer’s familiarity and visibility in the area, we

control for firm idiosyncratic risk. Hou and Moskowitz (2004) find that the idiosyncratic risk of

stocks is priced for small, less visible stocks, and Campbell and Taksler (2003) document that

idiosyncratic risk is positively associated with bond yield spreads. As such, firms that are more

visible are expected to have a lower cost of debt. Because firms headquartered in large

metropolitans are more visible and familiar to a larger set of investors than firms from rural areas,

by controlling for idiosyncratic risk we address the concern that our results could be driven by a

correlation between city centrality and firm familiarity. We follow Campbell and Taksler (2003)

and measure idiosyncratic risk as the standard deviation of the firm’s excess return over the market

5 Available at: http://www.som.yale.edu/faculty/am859/data.html

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portfolio. We use the prior 180 trading days to calculate idiosyncratic risk which we multiply by

ten for scaling purposes.

In our analysis, our primary measure of location are firms that are headquartered in remote

rural areas (Rural) and firms headquartered in central, large cities (Urban), with the latter being

the control group. To account for bookrunner−issuer relationships, we measure the distance

between each issuer and its lead bookrunner, with a bookrunner defined as local if it is based less

than 250 kilometers away from the issuer’s headquarters, and non-local otherwise. Similarly, to

capture issuer- investor and investors- bookrunner local relationships, we calculate the distance

between the location of each issuer and that of its institutional investors, and the distance between

the issuer’s bookrunner and each of its institutional investors. Thus, for each bond issue, we define

local institutions as those based less than 250 kilometers away from the issuer or the issuer’s

bookrunner. Local (non-local) institutional ownership is then computed as the book value of the

issue held by local (non-local) institutional investors in the quarter of the issue divided by the total

book value of the bond issued. We define an indicator variable, High (Low) local institutional

ownership to denote whether the fraction of local institutional ownership is above (below) the

sample median for local institutional ownership in that year.

We also account for prior, recent relationship between an issuer and its bookrunner, and

between institutional investors and the bookrunner. We define Repeat relationship with same

bookrunner as a prior offering by the issuer that shares a lead underwriter with the current

offering.6 Because more recent offerings compared to distant past ones contribute more to an

underwriter’s knowledge of the issuer, we impose a five-year cutoff period between the current

6 If the previous offering had 2 lead underwriters then the current offering is a relationship offering if either of the previous leads is used for the

current offering. Similarly, if the current offering has 2 lead managers then the offering is a relationship offering if either of the lead managers was

used in a previous offering.

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and past relationship offerings. Similarly, we calculate the fraction of holdings by institutions that

either purchased from the same issuer or the same bookrunner up to five years prior to the issue

and define High (Low) repeat institutional relationship to indicate whether the fraction of holdings

by institutional investors who have previously purchased bonds from the same issuer or

underwriter is above (below) the median for that year.

Finally, to conduct our secondary market robustness tests we use a set of variables that is based

on Elton et al. (2001), Campbell and Taksler (2003), and Chen et al. (2007), and others. These

include, accounting variables, bond specific and illiquidity measures, and several macroeconomic

variables. These variables are defined in Appendix I.

C. Summary Statistics

Table 1 presents the distribution of our sample across the two location groups, the MSA and

number of issues per MSA sorted by population size. Overall, there are 129 distinct MSAs (51

Urban and 78 Rural) in our sample ranging in population from 28,149 in Yazoo City, Mississippi

to about 18.9 million in New York City.

[Insert Table 1 about here]

A closer analysis of the data suggests that firms’ headquarters are clustered in a small number

of metropolitan areas. New York stands out as the dominant center, with about 10% of the bond

issues and 15% of the headquarters in our sample. Table 1, along with Figure 1, which displays

the distribution of public bond issuers, their bookrunners and institutional investors across the

continental US, show that most bond issuers are headquartered in or near the largest US cities.

Figure 1 also shows that bookrunners and institutional bond investors tend to cluster around the

same big cities. Rural areas are characterized by a significantly lower concentration of issuers,

investors and bookrunners and are also the most sparsely populated.

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[Insert Figure 1 about here]

[Insert Table 2 about here]

Table 2 presents the number of debt issues and the average at-issue yield spreads by year and

location groups (Urban or Rural) over 1998-2008. It shows that bond issues have increased from

185 in 1998 to 375 in 2008 and that firms headquartered in Urban areas issue the highest volume

of bonds in each year, totaling over 90% of the overall sample size. This high issuance volume by

firms headquartered in Urban metropolitans is likely due to the fact that more than 40% of all

publicly traded companies are located in the top 20 most highly populated US counties. For each

year, Rural firms report the highest average at-issue yield spread with an overall average of

182.982 basis points. In comparison, Urban companies have an average at-issue yield spread of

140.267 basis points. This 43 points difference between mean at-issue yield spreads is significantly

different from zero at the 1% level.

To get additional insights on the effect of location on the cost of corporate debt, we examine

the characteristics of Rural and Urban firms in more detail. Table 3 provides summary statistics

for the variables used throughout the analysis. Panel A, which compares firm and institutional

characteristics across Urban and Rural firms, shows that Rural firms are significantly smaller than

firms headquartered elsewhere. Consistent with Loughran and Schultz (2005), we find that Rural

firms are also significantly more levered than Urban companies as shown by the leverage ratios

of 26.926% and 20.357%. Interestingly, we find that they issue debt somewhat less frequently

throughout the sample period (with 84.121% multiple Rural issuers compared to 92.482% multiple

Urban issuers). Note, however, that a firm’s outstanding debt also includes bank loans and other

types of debt which could help to explain the higher leverage of Rural firms. Later in Table 10, we

indeed show that Rural companies are more likely to rely on bank loans than firms headquartered

elsewhere.

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[Insert Table 3 about here]

Consistent with Malloy (2005), Loughran and Schultz (2005) and others, we find that Rural

companies are more likely to be listed on Nasdaq, have higher idiosyncratic risk and less analyst

coverage compared to their Urban counterparts. Similar to Loughran and Schultz (2005), we find

that small, retail equity investors own a higher portion of the total shares outstanding in Rural

companies than Urban firms, whereas institutional investors tend to own a higher percentage of

shares in large city companies. This is consistent with the argument that institutional investors,

who are mainly concentrated in large metropolitans, are biased toward nearby, large city firms,

whereas the equity shares of remote rural companies are primarily held by local retail investors.

In contrast to to Rural issuers, Urban companies have a significantly higher proportion of their

bonds issued by local bookrunners and purchased by institutional investors who are based less than

250 kilometers away from their headquarters or the bookrunner’s office. To the extent that

geographic proximity reduces information asymmetry and facilitates the creation of social

networks, these local transactions could result in a comparative advantage for issuers based in

larger cities. Because both institutional investors and investment banks tend to be concentrated

around metropolitan areas, these findings are also consistent with Massa et al. (2009) that

institutional bondholders have a geographic bias in the ownership of local bonds which provides

for soft information advantages in the underwriting practices of investment banks (Butler, 2008).

In sum, the above findings suggest that rural firms are associated with lower visibility and

familiarity, greater information asymmetry and a lower concentration of both local bookrunners

and institutional ownership compared to their large-city counterparts. To the extent that the

distance from major concentrations of the investment community affect networking opportunities

and information sharing and are priced by bondholders, these factors would further exacerbate the

effect of an isolated rural location on the cost of debt.

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Panel B of Table 3 displays the relationship between headquarters location and issue related

characteristics. The average at-issue yield spread for Rural firms is 182.982 basis points compared

to Urban firms, with 140.267 basis points, with a statistically significant spread differential at the

1% level. Consistent with a smaller average asset size, the average issue size of Rural firms is

significantly smaller than that of urban firms. Interestingly, Rural firms tend to have lower average

debt rating than firms based in larger, major metropolitans. This could explain why, on average,

issues of Rural companies tend to have more protective covenants compared to issues made by

other firms.

Panel A of Table 4 presents the identity and characteristics of the 20 metropolitans that host

the headquarters of the most costly debt issues, whereas Panel B presents the characteristics of the

20 metropolitans with the lowest at-issue yield spreads. Each MSA in Table 4 is a subset of at least

one local issuer. Consistent with our conjecture that remote rural location results in a higher cost

of debt, we find that the metropolitans in Panel A are associated with Rural areas, whereas the

metropolitans in Panel B represent firms headquartered in larger cities (Urban). Consistent with

the findings in Table 1, issuers are clustered in a small number of large metropolitan areas, with

the ten most highly populated metropolitans being the dominant centers.

[Insert Table 4 about here]

Industry composition also differs between Rural and Urban firms. Table 4 reports the number

of local issuers for each MSA by industry in the manufacturing (SIC 20-39), banking and finance

(SIC 60-67), transportation and utilities (SIC 40-49), mining (SIC 10-14) and business services

(SIC 73, 81, 87 and 89) industries. Taken together, these seven industries account for 83% of Rural

and 75% of Urban companies. Mining, transportation and utility companies account for a greater

proportion of Rural than Urban firms, and manufacturing, business services and banking and

finance account for a greater proportion of Urban issuers.

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Panel B of Table 4 shows that Urban firms tend to cluster in the same sector of activity and

within proximity to other agglomerates of financial and business service firms, whereas as shown

in Panel A, Rural companies are more sparsely headquartered. These findings are consistent with

those in the extant urban economics literature which shows that urban firms’ headquarters tend to

cluster in areas with business services, other local headquarters and hiring pools. In contrast, rural

companies tend to locate in areas with a lower degree of regional product market competition (see,

e.g., Davis and Henderson, 2008; Fujita and Thisse, 2002).

Table 4 also sorts the sample by whether a firm’s headquarters is collocated in the same MSA

as its main production plant, research or service facility and therefore, derives benefits of intra-

firm monitoring such as lower communication costs with personnel. Panel A shows that while, in

general, Rural firms headquarters are collocated with their main plant, research or service facility,

Panel B shows that most Urban headquarters are not collocated with their major operational units.

These findings are consistent with the empirical literature on headquarters location (see, e.g.,

Duranton et al., 2004) which shows that for many regional companies, the headquarters

establishment is often collocated near an important production or service operation.

Bell et al. (2005) contend that firms are also attracted to locations with lower wage rates and

taxes and a higher quality of living. Table 4 shows that most Rural firms benefit from lower mean

wages and corporate tax rates compared to urban firms. Rural locations are also associated with

lower average commute times to work and lower median housing prices compared to Urban

metropolitans. Taken together, these findings, along with greater land availability and lower air

pollution suggest that Rural areas are characterized by lower congestion costs and cost of living.

Overall, Table 4 reflects some of the costs and benefits associated with headquarters location.

When faced with a tradeoff between labor costs, taxes, congestion costs, cost of living, intra-firm

communication and monitoring costs, supply of local services, local product market competition,

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and communication with other local businesses, firms differ as to the preferred mix and seem to

balance their visibility and distance from potential investors in the overall location decision.

III. Empirical Results

So far, we have shown that remote and less visible areas are characterized by higher financing

costs. However, these costs could also differ across firm locations if there are systematic

differences in firm and bond characteristics. To address this issue, in this section we use regression

analysis to examine the extent to which differences in at-issue yield spreads of corporate bonds

can be attributable to a firm’s headquarters location. To minimize the effect of outliers, we

winsorized all accounting variables at the 1% level.

A. Information Networks and the Cost of Debt

We begin the analysis by examining the relationship between our headquarters location

proxy for the quality of information networks, denoted by whether an issuer is headquartered in

an Urban or Rural area, and at-issue yield spreads of corporate bonds, controlling for firm and

bond-specific measures.7 The general specification of the regressions is as follows:

𝑆𝑝𝑟𝑒𝑎𝑑 = 𝛼 + 𝛽1𝑅𝑢𝑟𝑎𝑙 + 𝛽2(𝐹𝑖𝑟𝑚 characteristics) + β3

(Bond characteristics) + ε … … … . (1)

Column (1) of Table 5 contains the expected sign for each coefficient estimate, with our variable

of interest, Rural expected to have a positive sign. This is the case because social networks, closer

monitoring, information spillovers and the transfer of skills and ideas associated with large,

populous metropolitans could allow Urban firms to issue bonds at more competitive prices

compared to their remote Rural counterparts, resulting in lower at-issue yield spread, ceteris

7 The results are robust to alternative specifications of the dependent variable, including the logarithm of at-issue yield spreads.

19

paribus. The expected sign on the coefficients of the other independent variables is based on the

extant literature and in the interest of brevity will not be discussed here.

[Insert Table 5 about here]

Column (2) of Table 5 reports results of our primary regression specification where we use

Rural as a proxy for the quality of local information networks. They show that headquarters

location is statistically significant at the 1% level and has an economically meaningful impact on

at-issue yield spreads of corporate public bonds, even after controlling for bond and company

specific characteristics. Specifically, firms headquartered in a Rural MSA have at-issue yield

spreads that are, on average, 18.248 basis points higher than those of centrally headquartered

Urban companies, ceteris paribus. The coefficient estimates of the control variables all bear their

expected signs with most of them significant at conventional levels. The adjusted R2 of all the

models is at least 38%, indicating that the data explain a substantial portion of the variation in at-

issue yield spreads. More important, the relation between spreads and firm location is significant

at least at the 5% level for all model specifications. Thus, these findings corroborate the univariate

results that firms headquartered in larger and more populous cities have a lower cost of debt

financing.

Columns (3) and (4) segment the date into large and small firms, based on the median sample

asset size ($13.43 billion). To the extent that smaller firms are riskier than larger firms, then all

else equal, headquarters location should have a larger impact on bonds issued by smaller firms.

Consistent with this notion, we find that although the Rural dummy is positive and statistically

significant for both small and large firms, it is larger for small firms. Specifically, the spread

differential between small Rural and Urban firms is 31.533 basis points, whereas the differential

20

for large firms is only 14.124 basis points, suggesting that the information asymmetry is more

pronounced for smaller firms.

Similarly, Columns (5) and (6) segment the data into short and long maturity issues. Consistent

with the results in Columns (3) and (4), we find that riskier, longer maturity bond issues are

particularly costlier for Rural issuers, compared to their Urban counterparts, with a spread

differential of 12.218 basis points for short maturity issues versus 33.572 basis points for long

maturity ones. To the extent that large cities are home to a significantly larger investment

community, our results provide support for the notion that large metropolitans give local investors

a soft information and/or social network based advantage in assessing harder to evaluate bond

issues with higher risk of default.

Because firms based in large metropolitans are familiar to more investors, our results of a lower

spread in these areas could be due primarily to their higher visibility. On the other hand, highly

visible firms that are based in remote and smaller cities (e.g., Microsoft, headquartered in

Redmond, Washington) should be less affected by metropolitan size. As a result, issues related to

firm visibility and investor recognition may be masking the true relationship between the location

of companies and their cost of debt financing. We therefore account for familiarity and investor

recognition effects by including in our regression the Idiosyncratic risk measure discussed above

while including the usual bond and firm-specific control variables. In addition, we examine the

effect of information asymmetry on a firm’s cost of debt, conditional on its headquarters location,

by controlling for the number of analysts following the firm. In Column (7) of Table 5 we include

our information proxy, Number of Analysts, in addition to the firm-specific risk measure,

Idiosyncratic risk. Consistent with our expectations and with the extant literature (e.g., Campbell

and Taksler, 2003; and Mansi, Maxwell, and Miller, 2011), we find a negative and economically

meaningful association between the cost of debt and the number of analysts covering the firm and

21

a positive relationship between the cost of debt and idiosyncratic risk. Although the coefficients

of our location dummy variable declines in magnitude after controlling for these factors, it remains

both economically meaningful and statistically significant at the 5% level. Overall, these results

are consistent with our arguments that the information generated by local networks in large

metropolitans is economically significant and not subsumed by the number of local analysts or

attributes of the information environment of a firm per se.

To test whether firm level governance is driving our results, in Column (8) of Table 5 we

examine the effect of firm level governance on at-issue yield spreads using GIM index.8 We also

include Idiosyncratic risk and Number of Analysts along with the standard firm and bond control

variables. As expected, we find a positive relationship between at-issue yield spreads and the GIM

index. Although smaller in magnitude, the Rural dummy variable remains both economically

meaningful and statistically significant with a positive coefficient of 10.268 basis points. Thus, the

information contained in factors associated with investor recognition, firm governance and analyst

coverage, although explaining some of the cross sectional variation in the cost of debt does not

subsume those related to the location based network effects of companies. Perhaps most important,

our findings indicate that the positive externalities of information spillovers, social interaction and

monitoring benefits associated with the agglomeration of economic agents in large metropolitans

are priced by investors in corporate bond yields.

B. The Effect of Locality and Recent Prior Relationships on the Cost of Issued Debt

To determine the cross sectional characteristics of local bookrunning and institutional

relationships, we estimate cross sectional regressions of at-issue yield spreads on local (non-local)

bookrunning and local (non-local) institutional ownership, along with headquarters location, bond

8 The sample size is reduced to 3,741 bond issues due to lack of data availability for the GIM index variable.

22

and issuer characteristics. For each bond issue, a bookrunner is defined as local if it is based less

than 250 kilometers away from the issuer’s headquarters. Similarly, local institutional investors

are those located less than 250 kilometers away from the issuer or the issuer’s bookrunner. Local

institutional ownership is defined as the book value of the issue that is held by local institutions in

the quarter of the bond issue divided by the total book value of the bond issued. We then define

high (low) local institutional ownership if the percentage of local institutional ownership is above

(below) the sample median for the year.

Panel A of Table 6 reports the results. Most of the explanatory variables are significant in the

local and non-local bookrunning and institutional ownership regressions. These results indicate

that both local and non-local bookrunners and institutional investors adversely price remote Rural

issuers, smaller sized, lower rated issues, and issues made by more highly leveraged firms or

infrequent issuers. However, although the coefficients are significant in both local and non-local

bookrunning and ownership regressions, their magnitudes in the non-local bookrunner and the low

local institutional ownership regressions are significantly larger than those in the local

bookrunning and high local institutional ownership regressions. In particular, non-local

bookrunners and institutional investors assign a significantly higher yield to Rural issuers, smaller

size and longer maturity issues and one-time issuers compared to their local counterparts. These

findings indicate that geographic proximity gives both investors and underwriters a comparative

advantage in monitoring and assessing soft information about local issuers, especially riskier issues

and those made by Rural firms that have weaker connections to major investors and underwriters.

It is important to note however that while issuer- bookrunner or investor- bookrunner proximity

benefits Rural issuers, it is not enough to completely compensate them for their small local investor

base.

[Insert Table 6 about here]

23

Next we examine if a prior relationship between issuers, their lead bookrunners and

institutional investors significantly impact at-issue yield spreads. To the extent that geographic

distance impedes the portability of soft information across networks of economic agents, we expect

that a recent prior relationship would ameliorate its transmission. Results, which are reported in

Panel B of Table 6, suggest that a repeat relationship between issuers and their bookrunner or

between institutional investors and the same bookrunner and/or issuer lowers the at-issue yield

spread for Rural firms, smaller size and longer maturity issues. These findings further suggest that

repeat transactions between bookrunners, institutional investors and issuers result in a comparative

advantage in pricing risky, informationally opaque bonds. These networks of information,

however, are not sufficient to completely compensate for the long distance between the economic

agents within the network of rural issuers. These findings are consistent with the idea that while

these relationships are beneficial for lowering information costs, the portability and therefore,

quality of soft information is declining over geographic distance (see e.g., Agarwal and Hauswald,

2010; and Stein, 2002), which is especially detrimental to Rural issuers.

C. Information Networks and the Cost of Secondary Market Debt

Although most of the investment activity in fixed income occurs at issuance, there is a robust

and important secondary market in corporate bonds. If our hypothesis is correct, then information

networks should also matter in secondary market trading. While investment banks do not play a

role in secondary bond trading, access to private information should still matter in this market

because unlike markets with organized exchanges, corporate bonds are mostly traded over-the-

counter (OTC). Because OTC markets are generally viewed to have low liquidity and high

information asymmetry, access to private information is particularly important for traded bonds.

Studies by Froot et al. (1992) and others, suggest that such information asymmetry results in

24

situations where institutional investors share information or even mimic each other, either because

they trade together or because they receive correlated private information, perhaps from analyzing

the same indicators or sharing some preferences for securities with common characteristics, such

as liquidity or high visibility. To the extent that large cities are home to a large body of institutional

investors who may be exposed to similar private information about local firms, large metropolitans

may give these investors a soft information advantage compared to bonds issued by non-local,

remote rural companies. Indeed, Baik, Kang and Kim (2010) show that local institutional

ownership can predict future stock returns, and that such predictive ability is relatively weak for

non-local institutional investors. This suggests that local institutions possess better selection skills

that come from better exposure to soft information about local firms.

There are additional advantages to looking at secondary market trading. Because we can follow

a bond over time, it allows us to rule out cross sectional differences in uncontrolled characteristics,

including the bond’s liquidity. Importantly, if our documented spatial cross-sectional variation in

at-issue yield spreads is not due to information based market friction but, represents mispricing,

such mispricing should be arbitraged away in the secondary market.

To investigate whether information networks matter in the market for traded bonds, we

examine the impact of headquarters location on the cost of debt. The general specification of the

regressions and choice of control variables follow Chen, Lesmond and Wei (2007) and Campbell

and Taksler (2003) and is given by:

𝑆𝑝𝑟𝑒𝑎𝑑 =∝ +𝛽1𝑅𝑢𝑟𝑎𝑙 + 𝛽2𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 + 𝛽3𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 + 𝛽4(𝐵𝑜𝑛𝑑 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠)

+ 𝛽5(𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠) + 𝛽6(𝑀𝑎𝑐𝑟𝑜 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠) + 𝜀 … … . . (2)

The dependent variable is the traded bond yield spread, and the independent variable of interest

is the Rural headquarters location dummy that proxies for the quality of information networks.

The results are reported in Table 7. Column (1) contains the expected sign for each coefficient

25

estimate and Appendix I contains variable definitions. In the interest of space, and because the

focus of this paper is the proposed information network channel, we do not repeat their descriptions

here. Note that we do not include an expected sign for the CDS dummy variable because empirical

evidence about the impact of CDS on the cost of debt is mixed.9

[Insert Table 7 about here]

The coefficient estimates of the control variables all have the expected signs and are

statistically significant at conventional levels. The behavior of the volatility and liquidity measures

is informative. As expected, volatility and other credit risk measures, e.g., leverage, have a bigger

impact on the spreads of speculative bonds compared to investment grade bonds. With the

exception of accounting variables, this pattern is also true for our illiquidity, bond and macro

variables. Consistent with Ashcraft and Santos (2009), among others, we find an insignificant

average effect of CDS on bond spreads, and an adverse effect of CDS on the spreads of

informationally opaque, rural bonds. Importantly, however, we note that although declining in

magnitude, the coefficient on the Rural dummy variable remains positive and significant across

all regression models. Thus a remote location does seem to be related to decreased observability

that results with a higher yield spread for secondary market corporate bonds. Thus these findings

corroborate the earlier results that bonds issued by centrally based companies face lower

information costs that are reflected in lower yield spreads compared to their remotely based

counterparts.

IV. Robustness Checks

9 On one hand, the presence of CDS contracts has been found to be associated with greater bankruptcy risk, higher borrowing costs and lower

efficiency of the bond market (see, e.g., Ashcraft and Santos, 2009; Purnanandam, 2011; and Das, Kalimipalli and Nayak, 2012). On the other

hand, there is also evidence that CDS contracts may provide debt investors the ability to hedge risk and stimulate capital supply, thus enabling firms

to hold higher leverage and borrow at longer maturities (Saretto and Tookes, 2013).

26

In this section we provide a battery of robustness checks, including alternative sample selection

criteria, alternative measures of geographic location, and tests to rule out the presence of

endogeneity in our models. These various tests are reported in Tables 8, 9 and 10, and generate

qualitatively similar results to the ones reported above.

A. The Endogeneity of Location

Because the interaction between bond underwriters, the issuers they serve and the institutional

investors with which they have a relationship may be endogenous, we use two approaches to

address this possibility. We start with two stage least squares, where in the first stage, we use a

probit framework to model the choice of a Rural versus Urban issuer headquarters location, using

as an instrument the MSA based mean travel time to work (in minutes) taken from the American

Community Survey 5-year estimates.10 The rationale for this variable as an instrument is that it is

exogenous yet highly correlated with the size and centrality of the location in which the firm is

headquartered (correlation = 0.413). This is the case because large and dense metropolitans are

typically associated with a longer commute time from home to work compared to their rural

counterparts.

Thus, while it is plausible that firms may endogenously select where to locate their

headquarters or whether to network with nearby underwriters and investors, there is no reason to

expect that the area’s average commute time from home to work is likely to be related to a local

firm’s cost of debt after controlling for other factors, such as bond rating and issue size. We also

note that these results are robust to using an alternative instrument to denote a low volume issuing

MSA (less than 30 issues).11 We include in the first stage regressions all of the exogenous variables

10 Available at: http://factfinder2.census.gov/faces/nav/jsf/pages/community_facts.xhtml

11 Results are available upon request.

27

from the second-stage regression. We then take the predicted value of the Rural instrumented

variable from the first stage for use in the second stage regression, where the dependent variable

is the at-issue yield spread as before.

Panel A of Table 8 presents the first and second stage regressions results. The instrumental

variable is highly significant at the 1% level in the first stage, indicating that commute time is

highly correlated with the size and centrality of the city where the firm is headquartered and

thereby, justifying it as an appropriate instrument. The broad conclusions from the previous

sections go through when controlling for endogeneity. As the second stage results in Column (2)

suggest, holding everything else equal, the instrumental variable approach makes very little

difference in the coefficients of our control and (instrumented) location variable that remain highly

significant both economically and statistically. This underscores the important impact of

headquarters location on debt issuance costs. Thus, accounting for endogeneity supports our

conjecture that issuers based in larger and more visible locations have a comparative advantage

over their remote counterparts that appears to be the channel through which city centrality affects

the formation of local information networks and the debt issuance costs of local corporations.

[Insert Table 8 about here]

A second potentially powerful approach to deal with endogeneity is to examine changes in the

cost of debt for firms that experience an exogenous loss in analyst coverage as a result of brokerage

closures or mergers. Our approach is consistent with the recent literature that uses similar shocks

to analyst coverage as a quasi-natural experiment (e.g., Hong and Kacperczyk, 2010; and Derrien

and Kecskés, 2013). The literature also provides ample evidence that equity analysts play an

important informational role that can improve credit ratings (Cheng and Subramanyam, 2008) and

28

lower the cost of debt financing (Mansi, Maxwell and Miller, 2011).12 To the extent that analysts

are important information providers, we would expect the loss of coverage to be less detrimental

to issuers that already have stronger information networks in place, such as those based in central,

urban areas.

To test this hypothesis, we first identify bond issuing firms that lose an analyst because of

broker closures and mergers. Following the extant literature, we use I/B/E/S to identify analysts

who dropped coverage during 2000-2006 and match these events to Factiva press releases about

brokerage closures. For the same time period, we then match brokerage mergers from SDC to

I/B/E/S broker disappearances around the merger events. To measure the effect of analyst coverage

loss on the debt financing costs of issuers across Rural and Urban areas, we could simply compare

the average at-issue yield spreads before and after the coverage loss event for both groups.

However, events around the time of coverage loss, such as changes in interest rates or local

economic conditions, could also affect the pricing of newly issued debt. To account for this

possibility, we adopt a difference-in-differences approach.

For each issuing firm that lost analyst coverage as a result of brokerage mergers or closures,

we first measure the average at-issue yield spread of issues made up to two years before and two

years after the coverage loss, and calculate the change in the average at-issue yield spread as the

corresponding difference. This controls for the impact of time invariant firm characteristics on

debt pricing, such as industry membership. We then calculate the same change in average at issue

yield spreads for control firms over the same time period. We identify a control sample by using

all bond issues in our sample that are covered by analysts but did not lose coverage as a result of

the exogenous shocks during the same sample period. Our analyst coverage loss sample of firms

12 Johnston, Markov and Ramnath (2009) and Gurun, Johnston and Markov (2013) also provide related evidence about fixed income analysts.

29

with debt issues consists of 338 issuers over the years 1998-2008, from which 21 represent Rural

issuers and 317 represent Urban debt issuing firms, with 14 Rural and 199 Urban issuers used in

the control group.

Firm and issue characteristics can also change after the coverage loss event. Such changes can

potentially confound our analyses. To address this issue, we use the following model to control for

the impact of other determinants of debt financing costs:

∆(Average spreads) = α0 + α1Coverage Loss + β∆Controls + Year Dum. +ε … . . (3).

Where ∆(Average spreads) is the difference in average at-issue yield spreads between the

post- and pre-coverage loss periods. Coverage Loss is a dummy variable that takes the value of 1

for firms loosing analyst coverage as a result of brokerage closures and/or mergers and 0 for control

firms. Controls represent the usual set of control variables (see Table 5). Because we investigate

the change in debt financing costs, we use the lagged change in control variables in our regression

analyses. Finally, we include year and industry dummies to control for year and industry-specific

effects.13 Panel B of Table 8 reports the results. Column (1) contains a sample of Rural firms that

lost analyst coverage as a treatment group compared to Rural issuers that did not lose coverage as

a control group, and Column (2) compares Urban issuers that have lost coverage to Urban issuers

without coverage loss as a control sample. To conserve space we report only the results for the

Coverage Loss variable.

13 An important concern with the difference-in-differences methodology is that serial correlation of the error term can lead to understated standard

errors (Bertrand et al., 2004). In our regressions, however, we cluster standard errors at the MSA/CMSA level. This clustering not only accounts

for the presence of serial correlation within the same firm, but also for any arbitrary correlation of the error terms across firms in the same

CMSA/MSA in any given year as well as over time (see Petersen, 2009).

30

The results in Panel B indicate that while an exogenous loss of analyst coverage results in an

increase in the cost of debt for both Rural and Urban issuers compared to their respective control

groups, the spread increase is significantly larger for Rural firms, consistent with the notion that

Rural firms are more adversely affected by a forced loss of analyst coverage. Specifically, when

Rural firms lose analyst coverage, the average cost of debt increases by more than 13 basis points

compared to similar Rural firms that do not lose such coverage. In comparison, for Urban issuers

that lose coverage there is only a 6 basis points increase in the cost of debt compared to similar

large city firms that do not lose coverage. The size of the difference statistic between average

changes in the cost of debt for Rural and Urban issuers compared to their control groups is

economically meaningful and statistically significant at the 1% level. This supports our earlier

findings and suggests that, holding all other firm and issue characteristics constant, being located

in a large metropolitan, with greater concentration of other investors and information providers

has a significant impact on lowering a firm’s cost of debt.

B. Additional Robustness Checks

We continue with additional robustness checks in Table 9. To examine whether metropolitans with

major population changes are driving our results we run the primary regression dropping the ten

metropolitans that have exhibited the largest increase in population throughout the sample period.14

Results are reported in Column (1). Column (2) contains results when we estimate the primary

regression using only Rural firms and firms based in the ten high population growth metropolitans

that were excluded from Column (1). We find that the size of coefficients on the Rural dummy

14 These areas include New York-Northern New Jersey-Long Island, NY-NJ-PA; Los Angeles-Long Beach-Santa Ana, CA; Dallas-Fort Worth-

Arlington, TX; Houston-Sugar Land-Baytown, TX; Washington-Arlington-Alexandria, DC-VA-MD-WV; Miami-Fort Lauderdale-Pompano

Beach, FL; Atlanta-Sandy Springs-Marietta, GA; Las Vegas-Paradise, NV; Raleigh-Cary, NC; and Austin-Round Rock-San Marcos, TX

31

variable appears to be somewhat larger for high population growth areas; however, an unreported

Chow test (F-statistic=0.94) does not reject the null hypothesis that no difference exists between

the regression coefficients across the sub-sample groups of firms within low and high population

growth areas. Thus, the results reported in Columns (1) and (2) do not support the argument that

the spread differential between Rural and Urban issuers is driven by firms headquartered in high

population growth areas.

Column (3) reports results where we exclude firms headquartered in New York, Los Angeles,

Chicago, Washington DC and San Francisco from the regression as a way to examine whether our

findings are driven by issuers based in large financial centers. Although the magnitude of the

coefficient on the Rural dummy is somewhat reduced when we exclude financial centers from the

sample, the results continue to exhibit a positive and significant at-issue spread differential

between Urban and Rural issuers.15 Finally, in Columns (4) and (5), to examine whether changes

in population size that are common to all areas of the country throughout the sample period are

driving the results, we segment the data into bond issued during 1998-2002 and 2003-2008. The

results continue to show a higher cost of debt for Rural issuers compared to their Urban

counterparts.

C. Correcting for Self-selection in the Financing Choice

An issue that could confound our results is that of selection bias. Indeed, all the estimates are

based on the firm having already decided to issue bonds. This induces a selection bias if the

variables that determine the choice of location (or local networks) are the same ones that explain

the decision to issue bonds in the first place.16 To address this issue, we use a two-step procedure,

15 In additional, unreported tests we exclude from the sample companies in the banking, finance and business services industries (SIC codes 60-67

and 73, 81, 87 and 89), but continue to document qualitatively similar results. Results are available upon request.

16 We thank the referee for raising this important issue.

32

in which we first estimate a multinomial logit model of the probability of issuing bonds and then

we estimate an expanded specification of equation (1) that contains the inverse Mills ratio

(Heckman, 1979) constructed from the first stage.

We consider bond, equity and bank loans as the firm’s three main financing choices during the

sample period. Equity issuance data are from SDC and bank loan data are from LPC/DealScan.

The dependent variable is a discrete variable corresponding to four possible choices: issuing bonds,

issuing equity, taking a bank loan or no financing (normalized group). To make bank borrowing

and bond issuing more comparable, we only include bank loans with maturity longer than 3 years.

We require the total deal amount of each type of financing to be at least $20 million to be included

for each firm-year. If a firm issues the same type of securities multiple times in a year, we treat

them as one observation. Our choice of explanatory variables is based on Massa et al. (2013),

among others. Control variables are lagged values measured in the previous year. These variables

are defined in Appendix II.

The results are reported in Table 10. In the interest of space, we only report the coefficients of

the Rural dummy variable and the Inverse Mill’s Ratio (the full set of results are available upon

request). Note that because the control variables as well as the Rural dummy in the first stage are

chooser-specific (and not choice-specific), the first-stage coefficients in Panel A are estimated

separately for each choice. The coefficients for one choice (in this case, the choice of no external

financing) are normalized to 0.

The signs of the control variables in the first stage are largely consistent with the theory:

firms that experience high abnormal returns, have high market to book ratio, or high book leverage

are more likely to tap the equity market. In contrast, firms that experience high stock illiquidity,

low stock return volatility or have long distance to financial distress are less likely to borrow from

33

banks or issue equity and more likely to issue bonds, whereas firms that have large asset size or

high profitability are more likely to borrow from banks. These findings are consistent with the

interpretation that riskier firms are more likely to prefer arm’s length financing to bank financing

(see, e.g., Rajan, 1992). Interestingly, the coefficient on the Rural dummy variable is negative for

bond and equity issuance and positive for bank loan financing. This is consistent with earlier

findings in Loughran and Schultz (2005). Thus rural companies seem to substitute bond and equity

issuance with bank loans. The fact that the corresponding increase in the incidence of bank

borrowing more than offsets bond borrowing points to the possibility that there is a net positive

effect of Rural location on the firm’s leverage which may explain the higher leverage of rural

issuers compared to their urban counterparts.

[Insert Table 10 about here]

However, note that here we only measure the incidence of financing and not the net amount

of funds that is raised. More importantly, we find that even after correcting for selection in the

bond financing decision of firms, rural companies continue to exhibit a significantly higher cost

of debt compared to their urban counterparts. The negative and significant coefficient of the

inverse Mills ratio in Panel B of Table 10 indicates that OLS produces downwardly biased

estimates before self-selection is taken into account. Thus to the extent that firms may self-select

a financing strategy based on some aspects related to where they are located (or who they decide

to network with locally), our OLS estimates are biased against finding a significant spread

differential between rural and urban bond issuers.

V. Conclusion

This paper documents that issuer-underwriter-investor relationships appear to create local

networks that affect the pricing of securities. We proxy for the quality of information networks by

34

using an issuer’s headquarters location and examine whether the centrality of location results in a

pricing advantage. Specifically, examining public debt issues over 1998-2008, we find that bonds

issued by firms headquartered in large and central metropolitans that are home to a large base of

institutional investors and investment banks have lower at-issue yield spreads compared to bonds

issued by remote rural firms. This spread differential is more pronounced the further away rural

issuers are from their lead bookrunner and institutional investors and in the absence of a recent

prior relationships between the issuer, its lead underwriter and institutional investors.

Longer distance from the main underwriter and institutional investors and the lack of prior

recent relationships are also detrimental for firms issuing smaller sized and longer maturity issues

and for infrequent issuers. These findings are consistent with the interpretation that the geographic

concentration of investors and underwriters can provide local firms with a soft information

advantage that is reflected in lower issuance costs. This effect becomes especially pronounced for

riskier and more difficult to evaluate bonds because firms headquartered in large cities have better

access to information and stronger social ties with the large local investment community, who is

better able to assess, screen and monitor “difficult” local bond issues than those made by remotely

based companies.

Our results are robust to a host of sensitivity checks, sample selection criteria, control

variables, and endogeneity tests, including using an instrumental variable approach and a

difference-in-differences test based on an exogenous, natural experiment. Instead, our results are

likely driven by the existence of distinct networks of segmented investors created by an

asymmetric information flow. While it has long been noted that investment banking relationships

can create such information channels, we present new evidence that this has an effect on the pricing

of corporate bonds.

35

36

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41

Figure 1: Distribution of issuers, institutional investors and bookrunners across the continental US

This figure plots how the prevalence of corporate bond issuers, their bookrunners and institutional investors varies

geographically within the contiguous US states during 1998-2008.

25

30

35

40

45

50

65758595105115125

Lat

itud

e

Longitude

Geographical Variation of Issuers, Bookrunners and Institutional Investors

Issuer Institutional Investor Bookrunner

42

Table 1: Distribution of public bond issues across metropolitan statistical areas

This table summarizes the number of issues by headquarters location, based on population size data from the 2010 Census.

Classification MSA Population size # Issues

Urban

New York-Northern New Jersey-Long Island, NY-NJ-PA 18,897,109 408

Los Angeles-Long Beach-Santa Ana, CA 12,828,837 315 Chicago-Joliet-Naperville, IL-IN-WI 9,461,105 259

Dallas-Fort Worth-Arlington, TX 6,371,773 163

Houston-Sugar Land-Baytown, TX 5,946,800 149 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 5,965,343 211

Washington-Arlington-Alexandria, DC-VA-MD-WV 5,582,170 308

Miami-Fort Lauderdale-Pompano Beach, FL 5,564,635 83 Atlanta-Sandy Springs-Marietta, GA 5,268,860 122

Boston-Cambridge-Quincy, MA-NH 4,552,402 186

San Francisco-Oakland-Fremont, CA 4,335,391 124

Riverside-San Bernardino-Ontario, CA 4,224,851 138

Detroit-Warren-Livonia, MI 4,296,250 178 Phoenix-Mesa-Glendale, AZ 4,192,887 88

Seattle-Tacoma-Bellevue, WA 3,439,809 85

Minneapolis-St. Paul-Bloomington, MN-WI 3,279,833 38 San Diego-Carlsbad-San Marcos, CA 3,095,313 44

Tampa-St. Petersburg-Clearwater, FL 2,783,243 28

St. Louis, MO-IL 2,812,896 21 Baltimore-Towson, MD 2,710,489 63

Denver-Aurora-Broomfield, CO 2,543,482 32

Pittsburgh, PA 2,356,285 18 Portland-Vancouver-Hillsboro, OR-WA 2,226,009 39

San Antonio-New Braunfels, TX 2,142,508 21

Sacramento–Arden-Arcade–Roseville, CA 2,149,127 19 Orlando-Kissimmee-Sanford, FL 2,134,411 25

Cincinnati-Middletown, OH-KY-IN 2,130,151 36

Cleveland-Elyria-Mentor, OH 2,077,240 51 Kansas City, MO-KS 2,035,334 19

Las Vegas-Paradise, NV 1,951,269 40

San Jose-Sunnyvale-Santa Clara, CA 1,836,911 26 Columbus, OH 1,836,536 20

Charlotte-Gastonia-Rock Hill, NC-SC 1,758,038 33

Austin-Round Rock-San Marcos, TX 1,716,289 40 Indianapolis-Carmel, IN 1,756,241 19

Virginia Beach-Norfolk-Newport News, VA-NC 1,671,683 21

Nashville-Davidson–Murfreesboro–Franklin, TN 1,589,934 28 Providence-New Bedford-Fall River, RI-MA 1,600,852 58

Milwaukee-Waukesha-West Allis, WI 1,555,908 18

Jacksonville, FL 1,345,596 27 Memphis, TN-MS-AR 1,316,100 39

Louisville/Jefferson County, KY-IN 1,283,566 18

Oklahoma City, OK 1,252,987 14

Richmond, VA 1,258,251 16

Hartford-West Hartford-East Hartford, CT 1,212,381 63

New Orleans-Metairie-Kenner, LA 1,167,764 11 Raleigh-Cary, NC 1,130,490 26

Salt Lake City, UT 1,124,197 21

Buffalo-Niagara Falls, NY 1,135,509 41 Birmingham-Hoover, AL 1,128,047 23

Rochester, NY 1,054,323 46

Total Urban 3,919

Tulsa, OK 937,478 34

Rural Omaha-Council Bluffs, NE-IA 865,350 56

Little Rock-North Little Rock-Conway, AR 699,757 37 Boise City-Nampa, ID 616,561 29

Fayetteville–Springdale–Rogers, AR 464,623 47

Davenport-Moline-Rock Island, IA-IL 379,690 36

Peoria, IL 379,186 15

Saginaw-Saginaw Township North, MI 200,169 21

Monroe, LA 176,441 12 Jackson, MI 160,248 18

Other 104

Total Rural 409 All 4,328

43

Table 2: Public bond issues during 1998-2008

This table provides a summary of the number of bond issues and average at-issue yield spreads across location groups during 1998–2008.

The last column represent t-statistics comparing the at-issue yield spread means of Rural to those of Urban firms. ***, **, and * denote

statistical significance at the 1%, 5%, and 10% level, respectively.

Year

Rural

(1)

Urban

(2)

T-stat. (1)-(2)

(3)

N=17 N=168

1998 172.951 135.695 2.089**

N=24 N=259 1999 152.761 139.046 0.965

N=34 N=361

2000 160.211 144.558 0.953 N=54 N=586

2001 233.620 175.107 5.678***

N=65 N=528 2002 178.851 171.082 0.737

N=55 N=384 2003 162.928 152.969 0.601

N=46 N=293

2004 208.402 133.043 2.970*** N=27 N=251

2005 116.529 84.569 2.034**

N=19 N=317 2006 182.504 101.027 3.236***

N=44 N=421

2007 182.504 117.422 3.921*** N=24 N=351

2008 245.445 132.090 5.686***

Total N=409 N=3,919 4.618***

182.982 140.267

44

Table 3: Comparative descriptive statistics

A company is headquartered in an Urban area if the MSA has population of at least 1 million. Rural companies are located at least 250

kilometers away from Urban firms. Variables include: Asset size (Assets); Return on assets (ROA); Leverage (%); the proportion of

Nasdaq firms (% on Nasdaq); the proportion of issuers with more than one issue throughout the sample period (Multiple issuers); the

number of analysts covering the company at the end of a given year (Number of Analysts); the percentage of Outside (equity)

blockholders; Firm age (in years); Idiosyncratic risk (in %) is the standard deviation of daily excess returns over the CRSP value

weighted index for each firm’s equity over the 180 days preceding the bond issuance date; At issue bond yield spreads (Spread) in

basis points; issue proceeds (Proceeds) in $mil; Moody’s rating (Rating); Time to maturity in years; The percentage of non-investment

rated bonds (High yield issues); the percentage of bond issues accompanied by a prestigious underwriters (Prestigious underwriters);

the percentage of senior bond issues (% Senior); the percentage of issues with restrictive covenants (% Issues with restrictive

covenants); the percentage of issues with bookrunners based less than 250 kilometers away from the issuer (% issues with local

bookrunner); the average percentage of issue holdings by institutional investors based less than 250 kilometers away from the issuer

(% issue bought by local institutions); and the average percentage of holdings by institutional investors based less than 250 kilometers

away from the issuer’s bookrunner (% issue bought by institutions close to bookrunner).

Panel A: Firm and institutional characteristics

Variables Data Source Rural

(1)

Urban

(2)

T-stat.(1)-(2)

(3)

Assets ($billions) Compustat 7.462 18.171 -8.035***

ROA (%) Compustat 4.748 6.782 -8.945***

Leverage (%) Compustat 26.926 20.357 4.337***

% on Nasdaq SDC 56.359 44.467 6.027***

Multiple issuers (%) SDC 84.121 92.482 -3.368***

Number of analysts IBES 11.664 14.529 -4.193***

% Outside equity blockholders 13F 19.025 33.849 -5.375***

Firm age (years) Compustat 29.476 35.383 -1.275

Idiosyncratic risk (%) CRSP 2.716 1.931 3.298***

% issues with local bookrunner

SDC/Gazetteer

14.048

70.873

-6.278***

% issue bought by local institutions Lipper/Gazetteer 3.379 26.372 -16.356***

% issue bought by institutions close to bookrunner Lipper/SDC/Gazetteer 44.221 58.845 -3.254***

Max. sample size 409 3,919

45

Panel B: Issue characteristics

Variables Data Source Rural

(1)

Urban

(2)

T-stat. (1)-(2)

(3)

Spread (basis points) SDC 182.982 140.267 4.618***

Proceeds ($millions) SDC 189.845 276.950 -4.422***

Rating SDC 8.073 7.341 1.026

Time to maturity (years) SDC 11.047 10.480 1.525

% High yield issues SDC 17.248 16.839 0.856

% Senior SDC 42.636 47.326 -2.131**

% Prestigious underwriters SDC 80.238 80.719 -0.036

% Issues with restrictive covenants FISD 61.826 48.038 4.386***

Max. sample size 409 3,919

46

47

Table 4: Issuer characteristics by local average at-issue yield spreads

Panels A and B provide the location, industry composition and other characteristics of the top and bottom 20 CMSAs/MSAs in the average

at-issue yield spreads of bonds issued by firms headquartered in a given CMSA/MSA during 1998-2008. The industry composition of each

CMSA/MSA includes the manufacturing, banking and finance, Transportation/Utility and utility, mining and business services industries.

The number of local bond issuing firms by industry in a given CMSA/MSA is in parenthesis. Average annual wages for 2005 are from the

Bureau of Labor Statistics. The corporate tax burden rank is a number assigned to every US CMSA/MSA by the Tax Foundation, based on

Internal Revenue Service, Bureau of Economic Analysis and Census corporate tax data. Higher values denote a lower tax burden in a given

CMSA/MSA. Local plant, service or production facility represents operating units that are based in the same metropolitan as the respective

issuer’s headquarters location. “Yes” (“No”) means that all (only some) issuers headquartered in the MSA are collocated with their major

operating facility. Average travel time to work (in minutes) is from the Census Bureau. 2005 CMSA/MSA Median housing prices are from

the National Association of Realtors.

Panel A: Top 20 CMSAs/MSAs in average at-issue-yield spread

CMSA/MSA Industry (No. of local issuers by industry) Average

wage ($)

Corporate

tax

burden rank

Local

plant,

service or production

facility

Average

commute

(minutes)

Median

housing

Prices ($ ,000)

Spokane, WA Transportation/Utility (1), Manufacturing (1)

35,100 245 Yes 20.2 175.2

Duluth-Superior, MN-WI Transportation/Utility (2), Business

services (1)

34,600 274 Yes 19.9 177.7

Odessa-Midland, TX Manufacturing (1), Mining (1) 31,340 122 Yes 18.1 123.8

Huntington–Ashland, WV–KY–OH

Manufacturing (1), Mining (1), Business services (1)

31,150 312 Yes 21.5 153.1

Wichita, KS Transportation/Utility (1) 29,390 153 Yes 18.0 118.7

Augusta-Waterville, ME Transportation/Utility (1), Mining (1) 33,560 237 Yes 23.7 123.5

Boise City, ID Transportation/Utility (3), Mining (1),

Manufacturing (2)

35,330 133 Yes 21.5 153.8

Rapid City, SD Transportation/Utility (1), Mining (1) 29,490 87 Yes 17.6 87.4

El Paso, TX Transportation/Utility (1), Mining (1) 29,330 323 Yes 21.1 132.6

Des Moines, IA Transportation/Utility (4), Banking/Finance

(5), Mining (4),

Manufacturing (1), Business services (1)

37,080 38 Yes 19.8 149.3

Peoria-Pekin, IL Manufacturing (2), Business services (2) 35,070 147 Yes 20.4 119.4

Alexandria, LA Transportation/Utility (1), Business

services (2)

29,550 257 Yes 21.3 160.1

Portland, ME Transportation/Utility (1) 36,920 60 Yes 23.0 203.5

Dubuque, IA Transportation/Utility (1) 30,860 119 Yes 14.7 110.6

Omaha, NE Manufacturing (6), Transportation/Utility (5)

35,930 53 Yes 19.6 133.7

Evansville-Henderson, IN-KY Transportation/Utility (1), Mining (1) 33,110 154 Yes 20.3 85.2 Columbia, SC Transportation/Utility (1) 33,450 150 Yes 23.7 139.2

Yazoo City, MS Transportation/Utility (2), Manufacturing

(3)

32,140 96 Yes 22.9 134.9

Fergus Falls, MN Transportation/Utility (2) 32,580 285 Yes 21.0 140.2

Joplin, MO Transportation/Utility (1) 27,600 289 Yes 19.2 146.7

48

Panel B: Bottom 20 CMSAs/MSAs in average at-issue-yield spread

CMSA/MSA Industry (No. of local issuers by industry)

Average wage ($)

Tax burden

rank

Local plant or

production

facility

Average commute

(minutes)

Median housing

Prices ($

,000)

New York-Northern New Jersey-Long Island, NY-NJ -

PA

Manufacturing (92), Mining (2), Transportation/Utility (23), Banking/Finance (40),

Business services (14)

48,960 20 No 34.2 381.4

Los Angeles-Long Beach-

Santa Ana, CA

Mining (3), Manufacturing (11),

Transportation/Utility (7), Banking/Finance (7),

Business services (9)

41,680 59 No 28.4 333.9

Chicago-Joliet-Naperville, IL-

IN-WI

Mining (1), Manufacturing (39),

Transportation/Utility (17), Banking/Finance (7),

Business services (8)

41,500 23 No 31.0 199.2

Washington-Arlington-

Alexandria, DC-VA-MD-WV

Mining (1), Manufacturing (9),

Transportation/Utility (12), Banking/Finance (10),

Business services (9)

50,600 9 No 33.4 308.6

San Francisco-Oakland-

Fremont, CA

Mining (1), Manufacturing (13),

Transportation/Utility (8), Banking/Finance (9),

Business services (7)

50,120 3 No 28.3 493.3

Boston-Cambridge-Quincy,

MA-NH

Manufacturing (16), Transportation/Utility (4),

Banking/Finance (5), Business services (6)

50,550 8 No 30.5 332.6

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

Mining (2), Manufacturing (22), Transportation/Utility (12), Banking/Finance (5),

Business services (5)

41,500 32 No 27.9 215.9

Detroit-Warren-Livonia, MI Manufacturing (15), Transportation/Utility (4), Banking/Finance (1), Business services (6)

45,230 72 No 25.9 140.3

Houston-Sugar Land-

Baytown, TX

Mining (31), Manufacturing (19),

Transportation/Utility (18), Banking/Finance (2), Business services (9)

38,910 63 No 28.1 153.1

Dallas-Fort Worth, TX Mining (15), Manufacturing (12),

Transportation/Utility (15), Banking/Finance (1),

Business services (7)

39,990 33 No 26.9 140.5

Atlanta-Sandy Springs-

Marietta, GA

Manufacturing (9), Transportation/Utility (10),

Banking/Finance (3), Business services (5)

40,220 46 No 31.1 123.5

Miami-Fort Lauderdale-

Pompano Beach, FL

Manufacturing (4), Transportation/Utility (4),

Banking/Finance (1), Business services (3)

38,470 61 No 28.5 211.2

Phoenix-Mesa-Glendale, AZ Mining (4), Manufacturing (4), Transportation/Utility (2), Banking/Finance (1),

Business services (3)

37,870 74 Yes 26.5 137.0

Seattle-Tacoma-Bremerton, WA

Manufacturing (6), Transportation/Utility (6), Banking/Finance (3), Business services (2)

46,170 21 No 27.1 306.2

Minneapolis-St. Paul-

Bloomington, MN-WI

Manufacturing (14), Transportation/Utility (5),

Banking/Finance (4), Business services (2)

43,820 18 No 24.1 177.7

San Diego-Carlsbad-San

Marcos, CA

Manufacturing (5), Transportation/Utility (2),

Banking/Finance (3)

41,620 34 No 25.2 359.5

Mining (6), Manufacturing (4), Transportation/Utility (5), Banking/Finance (3),

Business services (4)

42,620 54 Yes 25.7 219.9

Pittsburgh, PA Mining (1), Manufacturing (7), Transportation/Utility (4), Banking/Finance (3),

Business services (1)

35,310 187 No 24.6 182.2

St. Louis, MO-IL Mining (2), Manufacturing (10), Transportation/Utility (3), Banking/Finance (1),

Business services (2)

37,900 67 Yes 24.6 127.1

Milwaukee-Waukesha-West Allis, WI

Manufacturing (8), Transportation/Utility (3), Banking/Finance (1), Business services (4)

39,070 56 Yes 21.3 193.4

Indianapolis, IN Manufacturing (5), Banking/Finance (5) 36,970 79 Yes 23.8 114.2

49

Table 5: Location and the cost of public debt

The dependent variable is the at-issue yield spread. A company is headquartered in an Urban area if the MSA has population of at least 1 million,

as defined by the 2010 census. Rural companies are located at least 250 kilometers away from Urban firms. Control variables include: issue size

estimated by the issue proceeds standardized by firm asset size (Size); return on assets in % (ROA); Moody’s rating (Rating); a Non-rated dummy that

takes a value of 1 if the bond is not rated, and 0 otherwise; leverage in % (Leverage); Firm age in years; a Nasdaq dummy (Nasdaq) to denote firms

listed on Nasdaq; a multiple issuer dummy (Multiple issuer) to denote bonds issued more than once over the sample period; bond time to maturity in

years (Time to maturity); a high-yield dummy (High Yield) to denote firms with non-investment grade debt (below Baa3); the predicted probability of

an issue being underwritten by a prestigious bond underwriter (Prestigious underwriter); a seniority dummy (Senior); callability dummy (Call); a

restrictive covenants dummy (Covenants); and the number of analysts covering the firm at the end of the year (Number of Analysts); Three-digit SIC

industry, year dummies and intercept are included but not reported. The data cover the period 1998-2008, with 4,328 bond issues, representing 904

firms. Robust standard errors (White, 1980) corrected for CMSA/MSA-year clustering are used to calculate p-values that appear in parentheses. ***,

**, * denote statistical significance at the 1%, 5% and 10% level, respectively.

Variable Expected Sign

(1)

Full sample

(2)

Large firms

(3)

Small firms

(4)

Short Maturity

(5)

Long maturity

(6)

Information asymmetry

(7)

Governance

(8)

Rural + 18.248*** 14.124*** 31.533*** 12.218*** 33.572** 10.121** 10.268**

(0.000) (0.007) (0.000) (0.000) (0.026) (0.017) (0.023) Size - -3.098*** -7.494*** -2.855** -5.119*** -2.128*** -2.835*** -2.984***

(0.000) (0.000) (0.025) (0.008) (0.000) (0.000) (0.000)

ROA - -0.314 -0.414 -0.662 -0.311 -0.408 -0.473 -0.516

(0.330) (0.269) (0.246) (0.332) (0.283) (0.151) (0.353)

Rating + 16.473*** 18.893*** 15.101*** 16.141*** 17.559*** 15.328*** 14.744***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Non rated dummy + 62.263*** 55.455*** 63.236** 58.769*** 64.479*** 63.136*** 63.328***

(0.000) (0.000) (0.043) (0.000) (0.000) (0.000) (0.000)

Leverage + 1.273*** 1.218*** 1.723*** 1.279*** 1.447*** 1.248*** 1.261*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Firm age - -0.002*** -0.003*** -0.002*** -0.000*** -0.001*** -0.002*** -0.002***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Nasdaq + 13.366 14.175 12.217 14.955 14.126 14.138* 13.893*

(0.233) (0.106) (0.275) (0.158) (0.146) (0.083) (0.089)

One time issuer + 27.886*** 28.138*** 33.595*** 27.369*** 29.748*** -32.147*** -31.462*** (0.000) (0.000) (0.007) (0.000) (0.000) (0.000) (0.000)

Time to maturity + 0.512*** 0.561*** 0.478*** 0.522*** 0.528***

(0.000) (0.000) (0.000) (0.000) (0.000) High yield dummy + 55.248*** 41.632*** 68.165** 53.749*** 57.996*** 51.640*** 50.117***

(0.000) (0.000) (0.011) (0.000) (0.000) (0.000) (0.000)

Prestigious underwriter - -3.289 -1.371 -5.194 -3.046 -3.057 -3.327 -3.549 (0.216) (0.231) (0.138) (0.211) (0.226) (0.132) (0.131)

Senior - -34.294* -36.441 -27.694** -32.156 -31.713* -32.138* -33.389* (0.081) (0.183) (0.020) (0.104) (0.091) (0.082) (0.093)

Call + 0.192** 0.318** 0.102** 0.181** 0.283** 0.193** 0.183*

(0.031) (0.031) (0.034) (0.028) (0.034) (0.044) (0.079) Covenants - -2.144* -3.218* -1.218* -2.045* -2.732* -2.271* -2.113*

(0.071) (0.083) (0.074) (0.089) (0.074) (0.054) (0.087)

Number of Analysts - -9.573*** -10.202*** (0.000) (0.000)

Idiosyncratic risk + 9.328* 9.326*

(0.081) (0.078) GIM index + 0.122

(0.827)

Adjusted 0.565 0.465 0.384 0.455 0.420 0.568 0.495

No. of observations 4,328 3,562 766 2,534 1,794 4,328 3,741

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Table 6: Determinants of local and non-local bookrunning and institutional ownership

Panel A: This table reports coefficients from regressing corporate at-issue yield spreads on headquarters location (Rural), firm and

issue characteristics for local (non-local) institutional ownership (columns 4 and 5) and local (non-local) bookrunning relationships

(columns 1 and 2). A company is headquartered in an Urban area if the MSA has population of at least 1 million, as defined

by the 2010 census. Rural companies are located at least 250 kilometers away from Urban firms, in MSAs with population

size of less than 1 million. A bookrunner is defined as local if it is based less than 250 kilometers away from the issuer’s

headquarters. Local institutions are those located less than 250 kilometers away from the issuer or the issuer’s bookrunner.

Institutional investor data is from Lipper’s eMAXX fixed income database. Local institutional ownership is computed as the book

value of the issue held by local institutional investors on the quarter of the issue divided by the total book value of the bond issued.

High (Low) local institutional ownership indicates that the fraction of local institutional ownership is above (below) the median.

SIC year dummies and intercept are included but not reported. Robust standard errors (White, 1980) corrected for MSA/CMSA-

year clustering are used to calculate p-values that appear in parentheses. Independent variables are as defined in Table 5.

Variable Local

bookrunner

(1)

Non-local

bookrunner

(2)

Test for

difference in coefficients

(3)

High local

institutional ownership

(4)

Low local

institutional ownership

(5)

Test for

difference in

coefficients

(6)

Rural 14.210** 20.786*** -1.84* 12.342*** 23.223*** -1.81*

(0.049) (0.001) (0.000) (0.000) Size -2.225*** -4.682*** 2.03** -2.317*** -4.465*** 1.98**

(0.006) (0.000) (0.003) (0.000)

ROA 0.700 0.157 0.76 0.426 0.187 0.54 (0.130) (0.780) (0.241) (0.352)

Rating 16.073*** 17.225*** -0.82 15.336*** 18.374*** -1.11 (0.000) (0.000) (0.000) (0.000)

Non rated dummy 63.335*** 62.404*** 0.25 60.881*** 68.712*** -1.04

(0.000) (0.000) (0.001) (0.000) Leverage 1.246*** 1.263*** -0.05 1.341*** 1.239*** 0.08

(0.000) (0.000) (0.000) (0.000)

Firm age 0.001 -0.002*** 1.26 0.002 0.001 0.02 (0.420) (0.000) (0.325) (0.234)

Nasdaq 22.161* 2.691 1.12 17.351 7.386 0.06

(0.061) (0.832) (0.126) (0.578)

One time issuer 17.137* 46.404*** -1.84* 14.364* 51.573*** -2.14**

(0.088) (0.000) (0.076) (0.000)

Time to maturity 0.243*** 0.744*** -2.03** 0.338*** 0.829*** -2.11** (0.000) (0.000) (0.000) (0.000)

High yield 49.102*** 57.158*** -0.53 47.824*** 56.189*** -0.58

(0.000) (0.000) (0.000) (0.000) Pres. underwriter -3.245 -3.441 0.07 -3.227 -3.957 0.15

(0.211) (0.262) (0.352) (0.267)

Senior -33.612* -32.189* -1.11 -32.189 -33.531 1.09 (0.092) (0.086) (0.213) (0.142)

Call 0.184** 0.191** -0.06 0.176** 0.188** -0.07

(0.029) (0.033) (0.028) (0.042) Covenants -2.138* -2.144* 0.01 -2.387* -2.468 0.06

(0.081) (0.086) (0.096) (0.135)

Adjusted 0.643 0.468 0.645 0.326

# Observations 2,835 1,493 1,863 561

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Panel B: This table reports coefficients from regressing corporate at-issue yield spreads on headquarters location (Rural), firm and

issue characteristics for issuers with repeat (one time) issuance activity with the same bookrunner up to five years prior to the issue

(columns 1 and 2) and institutional investors who are repeat (one time) buyers from the same bookrunner or issuer (columns 3 and

4). Institutional investor data is from Lipper’s eMAXX fixed income database. For every issue, we calculate the fraction of holdings

by institutions that either purchased from the same issuer or the same bookrunner up to five years prior to the issue. Throughout

the sample period, High (Low) repeat institutional relationship indicates that the fraction of holdings by institutional investors who

have previously purchased bonds from the same issuer or underwriter is above (below) the median. SIC, year dummies, and

intercept are included but not reported. Robust standard errors (White, 1980) corrected for state-year clustering are used to calculate

p-values that appear in parentheses. Independent variables are defined in Table 5.

Variable Repeat relationship

with same

bookrunner

(1)

Non-repeat issuer-

bookrunner

relationship (2)

Test for difference

in

coefficients (3)

High repeat institutional

relationship

(4)

Low repeat institutional

relationship

(5)

Test for difference

in

coefficients (6)

Rural 9.128** 22.236*** -2.38** 13.129*** 25.337*** -1.99**

(0.023) (0.000) (0.000) (0.000)

Size -2.129*** -3.337*** 1.99** -2.001*** -4.582*** 2.34** (0.002) (0.001) (0.000) (0.000)

ROA 0.679 0.216 0.84 0.541 0.338 0.36

(0.204) (0.512) (0.225) (0.218) Rating 18.271*** 17.219*** 0.64 16.193*** 17.217*** -0.72

(0.000) (0.000) (0.000) (0.000)

Non rated dummy 61.218*** 60.218*** 0.09 64.131*** 67.137*** -0.57 (0.000) (0.000) (0.001) (0.000)

Leverage 1.328*** 1.427*** -0.07 1.358*** 1.218*** 0.18

(0.000) (0.000) (0.000) (0.000) Firm age 0.002 0.002 0.01 0.003 0.001 0.05

(0.352) (0.136) (0.228) (0.137)

Nasdaq 10.261 7.336 0.61 11.336 8.236 0.06 (0.138) (0.217) (0.211) (0.424)

Time to maturity 0.337*** 0.872*** -2.05** 0.208*** 0.841*** -2.43**

(0.000) (0.004) (0.000) (0.000) High yield 50.327*** 55.129*** -0.12 48.938*** 53.218*** -0.34

(0.000) (0.000) (0.000) (0.000)

Pres. underwriter -4.183 -3.312 -0.11 -3.131 -3.583 0.21

(0.317) (0.362) (0.206) (0.267)

Senior -31.404* -31.003* -0.01 -33.126 -31.375 -0.94 (0.096) (0.082) (0.327) (0.221)

Call 0.174*** 0.197** -0.73 0.153** 0.191** -0.62

(0.006) (0.041) (0.031) (0.036) Covenants -3.004 -3.121 -0.03 -2.126* -3.152 0.28

(0.128) (0.216) (0.091) (0.143)

Adjusted 0.632 0.457 0.639 0.452

# Observations 2,417 1,911 1,521 903

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Table 7: Location and the cost of traded debt

The dependent variable is the traded bond yield spreads Bond Characteristics include Liquidity (Illiquidity), measured as the fraction

of days during a quarter where a bond does not trade, credit rating (Rating), term to maturity (Maturity), the log of the total amount

of par at issuance (Size), and coupon rate (Coupon). Firm characteristics include long term debt to total assets (LTD/TA), total

debt/total capitalization (TD/TC), operating income to sales (OI/Sales), and pretax interest coverage variables (Pr- tax Interest

Coverage). Macroeconomic variables include the one-year Treasury rate (1 yr. Treasury Rate), the difference between the two and

the ten year Treasury rates (10 Yr. – 2 Yr. Treasury Rate), and the difference between the 30-day Eurodollar and Treasury yields

(Eurodollar). We include the mean and standard deviation of on the daily excess return of the issuer’s equity (Issuer Stock Return

and Issuer Stock Volatility), and the mean and standard deviation of the daily stock market returns (Stock Market Return and Stock

Market Volatility). CDS denotes whether the issuing firm has a traded credit default swap.

Variable Sign (1)

Full Sample (2)

Investment Grade (3)

Speculative (4)

CDS (5)

Rural + 8.183*** 5.325*** 11.083*** 5.259*** (0.000) (0.000) (0.000) (0.000)

Rural X CDS 3.126*

(0.081) Illiquidity + 0.603*** 0.391*** 0.869*** 0.659***

(0.000) (0.000) (0.000) (0.000)

Rating + 12.821*** 10.337*** 14.238*** 12.249*** (0.000) (0.000) (0.000) (0.000)

Maturity + 0.328*** 0.141*** 0.274*** 0.116***

(0.000) (0.000) (0.000) (0.000) Size (log) - -0.039*** -0.026*** -0.092*** -0.084***

(0.000) (0.000) (0.000) (0.000)

Coupon (percent) + 6.446*** 6.291*** 6.835*** 6.439*** (0.000) (0.000) (0.000) (0.000)

LTD/TA (percent) + 2.849 2.396 3.281 1.397

(0.456) (0.419) (0.424) (0.426) TD/TC (percent) + 4.322*** 4.318*** 4.691*** 3.592***

(0.000) (0.000) (0.000) (0.001)

OI/Sales (percent) - -10.413*** -10.392*** -10.286*** -9.305*** (0.000) (0.000) (0.000) (0.000)

Pre Tax Interest Coverage < 5 - -1.385 -1.201 -1.082 -1.248

(0.450) (0.518) (0.538) (0.431) Pre Tax Interest Coverage < 10 - -1.696 -1.724 -1.468 -1.561

(0.552) (0.631) (0.637) (0.519) Pre Tax Interest Coverage < 20 - -1.963 -2.376 -2.293 -2.036*

(0.328) (0.370) (0.226) (0.095)

1 yr. Treasury Rate (percent) - -22.431*** -21.610*** -24.291*** -23.859***

(0.000) (0.000) (0.000) (0.000)

10 Yr. – 2 Yr. Treasury Rate - -13.219*** -11.474*** -14.381*** -12.209***

(0.000) (0.000) (0.000) (0.000) Eurodollar (percent) + 10.048*** 9.118*** 11.284*** 10.834***

(0.000) (0.000) (0.000) (0.000)

Issuer Stock Return (percent) - -34.056*** -31.338*** -39.349*** -33.120*** (0.000) (0.000) (0.000) (0.000)

Issuer Stock Volatility (percent) + 92.417*** 86.512*** 103.439*** 90.392***

(0.000) (0.000) (0.000) (0.000) Stock Market Return (percent) - -44.119*** -41.253*** -49.429*** -42.139***

(0.000) (0.000) (0.000) (0.000)

Stock Market Volatility (percent) + 29.563*** 28.618*** 31.213*** 28.012*** (0.000) (0.000) (0.000) (0.000)

CDS 4.648

(0.531)

Adjusted 0.392 0.389 0.371 0.391

# Transactions 15,178 11,134 4,044 15,178

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Table 8: Endogeneity tests

Panel A: This shows the results of a two stage regression model. Column (1) contains the results of a probit estimation where

we model the choice of a Rural versus Urban headquarters location. In the first stage, we use as an instrument for a firm’s

headquarters location the CMSA/MSA based commute time from home to work (in minutes) data from the US Census Bureau.

We include in the first stage regression all of the exogenous variables (except for location variables) from the second-stage

regression (see list of independent variables, dummies and their definitions in Table 5). We then take the predicted value of the

Rural dummy from the first stage for use in the second stage OLS regression in Column (2), where the dependent variable is

the at-issue yield spread as before. Robust standard errors (White, 1980) corrected for CMSA/MSA-year clustering are used to

calculate p-values that appear in parentheses. ***, **, * denote statistical significance at the 1%, 5% and 10% level,

respectively.

Variable First stage

(1)

Second Stage

(2)

Rural (instrumented) 15.947***

(0.000)

Number of Analysts -0.136*** -9.129*** (0.000) (0.000)

Outside blockholders -0.118* -1.047*

(0.089) (0.091) Size -0.126*** -3.182**

(0.074) (0.069)

ROA -0.016 -0.326 (0.123) (0.361)

Rating 0.016 17.225***

(0.438) (0.000) Non rated dummy 0.028 64.193***

(0.307) (0.000)

Leverage 0.017* 1.539*** (0.082) (0.000)

Firm age -0.021 -0.003***

(0.131) (0.000) Nasdaq 0.138 12.327

(0.278) (0.379)

One time issuer 0.138* 26.448*** (0.093) (0.000)

Time to maturity -0.242 0.563*** (0.650) (0.00)

High yield dummy -0.002 56.942***

(0.849) (0.001) Prestigious underwriter -0.103 -5.161

(0.529) (0.439)

Senior 0.033 -34.228* (0.431) (0.096)

Call 0.217 0.218**

(0.339) (0.058) Covenants 0.244 -2.046*

(0.106) (0.078)

Instrument: Commute time to work -0.338*** (0.006)

Pseudo/Adjusted 0.271 0.553

No. of observations 4,117 4,117

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Panel B: This Table reports regression results from a natural experiment of firms that lost analyst coverage as a

result of brokerage house mergers or closures during the years 2000-2006. We use the following equation for the

period t-2 to t+2, where t is the coverage loss year:

∆(𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑡 𝑖𝑠𝑠𝑢𝑒 𝑦𝑖𝑒𝑙𝑑 𝑠𝑝𝑟𝑒𝑎𝑑𝑠) = 𝛼0 + 𝛼1𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝐿𝑜𝑠𝑠 + 𝛽∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑌𝑒𝑎𝑟 𝐷𝑢𝑚𝑚𝑖𝑒𝑠 + 𝜀

∆(Average at issue yield spreads) is defined as the difference in average at-issue yield spreads between the post-

and pre-coverage loss periods. Coverage Loss is a dummy variable with value 1 for firms loosing analyst coverage

as a result of brokerage mergers or closures and value 0 for control firms. Controls represent the usual set of control

variables (see, e.g., Table 5). Because we investigate the change in debt financing costs, we likewise use the lagged

change in control variables in our regression analyses. All other variables are as defined in Table 5. The sample of

bond issuing firms covered by analysts during 1998-2008 includes a total of 35 (516) rural (urban) issuers, from

which 21 (317) experience brokerage mergers/closures and resulting coverage losses during 2000-2006, and 14

(199) experience no such loss. Our treatment sample includes all bond issues by firms covered by equity analysts

over 1998-2008 that experience brokerage house mergers or closures and result in loss in analyst coverage, whereas

issuers in our control group do not experience such exogenous coverage losses. Column (1) contains the results of

the impact of analyst coverage loss on Rural issuers, with Rural issuers that experience no such exogenous loss as

controls. Similarly, Column (2) contains the results of the impact of analyst coverage loss on Urban issuers, with

Urban issuers that experience no such exogenous loss as a control sample. To conserve space we only report the

results for the Coverage Loss variable. Robust standard errors (White, 1980) corrected for CMSA/MSA-year

clustering are used to calculate p-values that appear in parentheses. ***, **, * denote statistical significance at the

1%, 5% and 10% level, respectively.

Variable

Rural issuers

(1)

Urban issuers

(2)

Test for difference

in coefficients

(3)

Coverage Loss 13.492*** 6.117*** 5.318***

(0.000) (0.000)

Adjusted 0.229 0.385

No. of observations 130 1,909

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Table 9: Robustness

This table estimates coefficients from regressing corporate at-issue yield spreads on headquarters location (Rural),

firm, and various control variables. A company is headquartered in an Urban area if the MSA has population of at

least 1 million, as defined by the 2010 census. Rural companies are located at least 250 kilometers away from Urban

firms, in MSAs with population size of less than 1 million. Control variables are described in Table 5. Three-digit SIC

industry, year dummies and intercept are included but not reported. The data cover the period 1998-2008, with 4,328

bond issues, representing 904 firms. Robust standard errors (White, 1980) corrected for CMSA/MSA-year clustering

are used to calculate p-values that appear in parentheses. ***, **, * denote statistical significance at the 1%, 5% and

10% level, respectively.

Variable Areas without

high population

growth

(1)

High

population

growth areas

(2)

Excluding

financial

centers

(3)

1998-2002

(4)

2003-2008

(5)

Rural 15.137*** 22.382*** 13.661** 11.291*** 21.071***

(0.000) (0.000) (0.024) (0.000) (0.000)

Size -3.169*** -3.139*** -2.327*** -2.486*** -3.118***

(0.000) (0.000) (0.000) (0.000) (0.000)

ROA -0.248 -0.291 -0.495 -0.396 -0.348

(0.341) (0.446) (0.196) (0.312) (0.379)

Rating 16.104*** 16.478*** 16.539*** 15.381*** 16.892***

(0.000) (0.000) (0.000) (0.000) (0.000)

Nonrated dummy 61.589*** 62.476*** 63.982*** 59.875*** 63.073***

(0.000) (0.000) (0.000) (0.000) (0.000)

Leverage 1.128*** 1.236*** 1.451*** 1.284*** 1.264***

(0.000) (0.000) (0.000) (0.000) (0.000)

Firm age -0.001*** -0.001*** -0.003*** -0.000*** -0.002***

(0.000) (0.000) (0.000) (0.000) (0.000)

Nasdaq 12.496 13.447 11.581 12.370 13.357

(0.307) (0.216) (0.321) (0.348) (0.241)

One time issuer 25.316*** 27.491*** -27.285*** 24.365*** 27.903***

(0.000) (0.000) (0.001) (0.000) (0.000)

Time to maturity 0.531*** 0.526*** 0.511*** 0.519*** 0.510***

(0.000) (0.000) (0.000) (0.000) (0.000)

High yield dummy 56.184*** 55.396*** 55.423*** 54.782*** 55.001***

(0.000) (0.000) (0.000) (0.000) (0.000)

Prestigious underwriter -2.164 -1.745 -4.352 -3.935 -3.271

(0.392) (0.267) (0.143) (0.328) (0.229)

Senior -34.126* -34.298* -14.703*** -34.228* -34.047*

(0.093) (0.087) (0.000) (0.076) (0.086)

Call 0.194** 0.203** 0.217** 0.146** 0.193**

(0.038) (0.026) (0.041) (0.049) (0.038)

Covenants -2.274 -2.146* -2.136* -2.136* -2.168*

(0.121) (0.072) (0.085) (0.088) (0.059)

Adjusted 0.469 0.451 0.483 0.457 0.462

No. of observations 2,674 2,063 2,914 2,096 2,232

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Table 10: Financing choice self selection model

Panel A: This table reports the first stage of a multinomial logit model that describes the firm’s choice of financing. The dependent

variable is a discrete variable corresponding to four choices: issuing bonds, issuing equity, taking a bank loan or no financing

(normalized group). Control variables are defined in Appendix II and include: Bond holding fraction, Stock turnover, Stock holding

fraction, Abnormal return, Amihud’s illiquidity, Stock return volatility, Asset tangibility, Firm size, Profitability, R&D dummy,

Altman’s Z score, Market-to-book ratio, and Leverage (in %). To conserve space we only report the results for the Rural dummy

variable. Three-digit SIC industry dummies, year dummies and intercept are included but not reported. Robust standard corrected

for CMSA/MSA-year clustering are used to calculate p-values that appear in parentheses. ***, **, * denote statistical significance

at the 1%, 5% and 10% level, respectively.

Panel B: This table provides the estimated coefficients (and p-values) corresponding to the self selection model based on Massa,

Yasuda and Zhang (2013). The dependent variable is the public bond at-issue yield spread. Independent variables are described in

Table 5. To address the potential endogeneity in the choice of financing instrument, we run the first stage regression reported above

in column (1), in which the dependent variable =1 for bond issues and 0 otherwise, and include the corresponding Inverse Mill’s

Ratio in the second stage regression below. Three-digit SIC industry dummies, year dummies and intercept are included but not

reported. To conserve space we report only the results for the Rural dummy variable and the Inverse Mill’s Ratio. Robust standard

errors (White, 1980) corrected for CMSA/MSA-year clustering are used to calculate p-values that appear in parentheses. ***, **,

* denote statistical significance at the 1%, 5% and 10% level, respectively.

Variable Bond

(1)

Equity

(2)

Bank loan

(3)

Rural -1.227** -1.717* 1.525**

(0.042) (0.075) (0.038)

Pseudo 0.197 0.197 0.197

No. of observations 7,354 7,354 7,354

Variable (1)

Rural 18.692***

(0.000)

Inverse Mill’s Ratio -2.482***

(0.000)

Adjusted 0.565

No. of observations 4,328

2R

2R

57

Appendix I: Variables used in the secondary market regressions

Coupon: the annual percentage rate payable on the bond.

Maturity: the number of remaining years from time t to the bond maturity date.

LTD/TA: long term debt divided by the sum of the market value of equity and the book value of debt. The market

value of equity is computed by multiplying the stock price at the end of each year by the number of shares outstanding.

TD/TC: calculated as the sum of long-term debt in current liabilities and average short term borrowings, divided by

the sum of total liabilities and the market value of equity. The market value of equity (CRSP) is obtained 1 day prior

to the bond transaction date.

Size: the log of the total amount of par at issuance.

OI/Sales: operating income before depreciation divided by net sales.

Pre Tax Interest Coverage: the sum of operating income after depreciation and interest expense divided by interest

expense.

Pre Tax Interest Coverage <5: indicator variable equal to 1 if the Pre Tax Interest Coverage is less than 5 and 0

otherwise.

Pre Tax Interest Coverage <10: indicator variable equal to 1 if the Pre Tax Interest Coverage is less than 10 and 0

otherwise.

Pre Tax Interest Coverage <20: indicator variable equal to 1 if the Pre Tax Interest Coverage is less than 20 and 0

otherwise.

Illiquidity: measured as the fraction of days during a quarter where a bond does not trade.

1 yr. Treasure Rate: is the one year Treasury rate.

10 yr. – 2 yr. Treasury Rate: the difference between the 10-year and 2-year Treasury rates that describes the slope of

the yield curve.

Eurodollar: measured as the difference between the 30-day Eurodollar and 3-month Treasury bill rate.

Issuer Stock Return: measured as the mean of daily excess returns (in percentage points) relative to the CRSP value

weighted index for each firm’s equity over the 180 days preceding the bond transaction date.

Issuer Stock Volatility: measured as the standard deviation of daily excess returns (in percentage points) relative to

the CRSP value weighted index for each firm’s equity over the 180 days preceding the bond transaction date.

Stock Market Return: measured as the percentage mean of daily market returns, where the market is defined as the

CRSP value-weighted index over the 180 days preceding the transaction date.

Stock Market Volatility: measured as the percentage standard deviation of daily market returns, where the market is

defined as the CRSP value-weighted index over the 180 days preceding the transaction date.

CDS: an indicator variable to denote whether the firm has a traded credit default swap.

58

Appendix II: Variables used in the financing choice self selection model

Bond Holding Fraction: measured as the sum of holdings of firm i’s bonds by all institutional investors included in

the Lipper dataset divided by firm i’s total debt outstanding.

Stock Holding Fraction: measured as the sum of holdings of firm i’s common shares by all institutional investors

included in the CDS/Spectrum dataset divided by firm i’s total shares outstanding.

Stock Turnover: the average ratio of firm i’s stock total trading volume divided by the firm’s number of shares

outstanding.

Abnormal Return: is the cumulative abnormal return measured relative to a CRSP value-weighted market model

regression and estimated using the second year prior to the forecast yea.

Amihud’s Illiquidity: an illiquidity measure downloaded from Professor Joel Hasbrouck’s website

(http://pages.stern.nyu.edu/~jhasbrou/Research/GibbsEstimates2006/).

Stock Return Volatility: measured as the 12-month rolling sample deviation of monthly stock returns.

Asset Tangibility: measured as the firm’s net property, plant and equipment, divided by book assets.

Firm Size: is the firm’s log of asset size.

Profitability: measured as the firm’s operating income before depreciation over book assets.

R&D Dummy: a dummy variable equal to 1 if R&D expenditure data is non-missing and 0 otherwise.

Market-to-Book: the market value of assets over book assets.

Leverage: long term debt divided by the sum of the market value of equity and the book value of debt.

Altman’s Z score: 3.3 * pretax income + sales + 1.4 * retained earnings + 1.2 * (current assets – current liabilities)/book

assets.